Upload Data

Expression Data

An unlabelled peptide expression data file called “pep_edata.csv” was uploaded to pmart. The column that designates unique molecules was marked as “Mass_Tag_ID”. The original scale of the data was abundance and was changed to log2. The value to denote missing data was “NA” and the expression data was not already normalized.

Biomolecule Information

An associated biomolecule information file was also uploaded called “pep_emeta.csv” and the protein identifier column was designated as “Protein”.

Sample Information

An associated sample information file was also uploaded called “pep_fdata.csv”. Trimmed sample names were not used. The column in the sample information file which indicates sample names was designated as “SampleID”.

Group Samples

Grouping Information

This table summarizes all the user specified main effects or covariates in pmart. The “Selected Column Name” denotes the name of the column in the sample information file assigned as a main effect or covariate.

Main Effect or Covariate Selected Column Name
First Main Effect Condition
Second Main Effect None selected
First Covariate None selected
Second Covariate None selected

Number of Samples per Main Effects

Data Summary

Summary Table

The first column in the table below denotes a property of the peptide data, and the “Data” column states that property’s value.

Data
Class pepData
Unique SampleIDs (f_data) 10
Unique Mass_Tag_IDs (e_data) 13022
Unique Proteins (e_meta) NA
Missing Observations 35317
Proportion Missing 0.271
Samples per group: Infection 7
Samples per group: Mock 3

Missing Value Table

In the table below, the first column denotes the sample and the second is the missing number of observations. The third column represents the second as a percentage of the total number of observations for that sample.

Missing Observations Proportion Missing
Infection2 2711 0.208
Infection3 3447 0.265
Infection4 3978 0.305
Infection6 3535 0.271
Infection7 2896 0.222
Infection8 6014 0.462
Infection9 4953 0.380
Mock1 2682 0.206
Mock2 2551 0.196
Mock3 2550 0.196

Filter

Filters were applied. A total of 6 filters were applied. See the table below for a descriptions of the filters and the order.

Summary of Applied Filters

Order Filter Type Parameters Summary
1 Molecule Filter Biomolecule Min Number Molecules: 2 1814 biomolecule(s) were filtered.
2 Proteomics Filter Biomolecule Min Number of Peptides: 2 & Degenerate Peptides Removed: Yes 708 biomolecule(s) were filtered and 952 protein(s) were filtered.
3 CV Filter Biomolecule Max CV: 150 2 biomolecule(s) were filtered.
4 imd-ANOVA Filter Biomolecule Min ANOVA: 2 & Min G-Test: 3 3866 biomolecule(s) were filtered.
5 rMD Filter Sample P-Value Threshold: 0.001 & Metrics Used: MAD, Kurtosis, Skewness, Correlation 1 sample(s) were filtered.
6 Custom Filter Sample or Biomolecule 1 sample(s) were filtered. 0 biomolecule(s) were filtered. 0 protein(s) were filtered.

Molecule Filter

A molecule filter was applied to the data, which removes biomolecule(s) (Mass_Tag_IDs) not having at least the minimum number of samples (Min Number Molecules).

CV Filter

A coefficient of variation (CV) filter was applied to the data which removes biomolecule(s) (Mass_Tag_IDs) with a CV greater than the threshold (Max CV).

imd-ANOVA Filter

An ANOVA filter can be applied to the data which removes biomolecule(s) (Mass_Tag_IDs) not having at least a minimum number of non-missing values per group (Min ANOVA). Additionaly, an IMD (independence of missing data) filter can be applied to the data, removing biomolecules not having at least a certain number of non-missing values (Min G-Test) in at least one of the groups.

Proteomics Filter

A degenerate peptide filter (Degenerate Peptides Removed) can be applied to the data, which identifies biomolecule(s) (Mass_Tag_IDs) not belonging to one protein. Additionally, a protein filter can be applied to the data which identifies proteins not having at least a minimum number of peptides (Min Number of Peptides) mapping to them.

rMD Filter

A robust Mahalanobis distance (rMD) filter was applied to the data, removing sample(s) (SampleIDs) with an associated rMD-associated p-value less than the threshold (P-Value Threshold). Metrics used to calculate the p-value are also included (Metrics Used).

Custom Filter

Custom filters can be used to remove samples, biomolecules, or proteins.

Normalization

Peptide data was normalized.

SPANS

SPANS was run to determine optimum normalization parameters.

Manual

Manual normalization was used to normalize the data.

Attribute Value
Subset Function all
Subset Parameters
Normalization Function mean

Statistical Analysis

The analysis step was run.

The following groups were compared: Infection_vs_Mock. Reported below are the parameters used in the statistical analysis, followed by the number of siginificant biomolecules for each comparison.

Attribute Value
Test Method anova
Multiple Comparison Adjustment holm
Significance Threshold 0.05
Comparison Up_total Down_total Up_anova Down_anova Up_gtest Down_gtest
Infection_vs_Mock Infection_vs_Mock 1509 2742 1509 2742 0 0